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Extrapolative prediction using physically-based QSAR
Surflex-QMOD integrates chemical structure and activity data to produce physically-realistic models for binding affinity prediction . Here, we apply QMOD to a 3D-QSAR benchmark dataset and show broad applicability to a diverse set of targets. Testing new ligands within the QMOD model employs automat...
Gardado en:
| Publicado en: | J Comput Aided Mol Des |
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| Main Authors: | , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado: |
Springer International Publishing
2016
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| Assuntos: | |
| Acceso en liña: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4796382/ https://ncbi.nlm.nih.gov/pubmed/26860112 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/s10822-016-9896-1 |
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